In-vivo monitoring is typically done using electrochemical sensors, but those are only suited for high-concentration analytes, e.g., glucose. It remains a problem to detect low-concentration analytes with high specificity in a complex biological matrix (or a derivative thereof), e.g., in blood plasma, whole blood, skin interstitial fluid, gland fluid, tear fluid, mucus, mucous glands, saliva, cells, etc.
A recent development is biosensors based on single-molecule detection principles, where the binding of an analyte molecule to a probe results in a detection signal with a digital characteristic, e.g., an analyte molecule is present or is not-present. Single-molecule detection has distinct advantages over ensemble-averaged techniques because it yields statistical distributions of molecular properties instead of averages and reveals rare and unsynchronized events. An example of such a biosensor with analyte-receptor is illustrated is FIGS. 1A-B. Biosensing probe 11 carries at least one analyte-receptor 12 on its surface. Biosensing probe 11 can be a molecular or a supramolecular moiety, a particle, a surface, a pore, a tip, etc. Attached to the probe 11 is an analyte-receptor 12 which is selected to bind to analyte 13. FIG. 1A shows the probe configurations with a free analyte 13 and FIG. 1B shows the probe configuration with a bound analyte 13. The transition from unbound to bound states is characterized by an on-rate kon, while the transition from bound to unbound states is characterized by an off-rate koff.
One problem of the biosensor in FIGS. 1A-B is that analytes are typically detected by using recognition molecules with strong binding affinities, such as, e.g., antibody-like molecules. These molecules bind strongly and therefore have low off-rates (low koff). A problem is that low off-rates result in extended blocking times because the bound analyte occupies the receptor. This will not only limit the statistics that can be gathered in a certain time-frame, but will also result in a slow response time of the sensor, which is problematic for real-time monitoring applications.
Another problem with the biosensor in FIGS. 1A-B is accuracy. A biosensor system should be accurately known in order to avoid misinterpretation of the measured signals, e.g., baseline characteristics (e.g., drift), noise, sensitivity, signal-to-noise ratio. A difficulty is that signal and noise characteristics can change over time, e.g., due to variations in an optical excitation system (e.g., incident power), variations in an optical detection system (e.g., focal drift), variations in a probe property (e.g., due to temperature changes), variations in molecular properties on a probe (e.g., number of active molecules on the probe, mobility of signalling molecules on the probe surface), or variations of a property of the sample fluid (e.g., viscosity). These variations are easily misinterpreted as being signals, which reduces the accuracy and reliability of the sensor.
Another problem is that detection of different analytes requires the use of different recognition molecules on the particle, and therefore the development of novel procedures to couple recognition molecules to the probe.